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Status: Unchecked ×Keyword: BIG-Bench ×Clear all

6 claims from 3 papers

1. Computer Science › Topic Modeling

   PaLM: Scaling Language Modeling with Pathways

   Chowdhery, Narang, Devlin et al. · arXiv (Cornell University) · 2022

   The authors trained PaLM, a 540-billion-parameter language model, and report state-of-the-art few-shot results on hundreds of benchmarks, plus analyses of scaling, bias, toxicity and memorisation.

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   1. UncheckedThe authors report that scaling a language model up to 540 billion parameters gave state-of-the-art few-shot results on hundreds of benchmarks.“We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks.”
   2. UncheckedMany BIG-bench tasks showed sudden, steep gains in performance when the model reached the largest size the authors trained, PaLM 540B.“A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model.”
   3. UncheckedOn some tasks, the 540-billion-parameter PaLM model beat the finetuned state of the art on multi-step reasoning and average human performance on BIG-bench.“On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark.”

2. Computer Science › Topic Modeling

   Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

   Süzgün, Nathan, Schärli et al. · arXiv (Cornell University) · 2022

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   1. Unchecked“We find that applying chain-of-thought (CoT) prompting to BBH tasks enables PaLM to surpass the average human-rater performance on 10 of the 23 tasks, and Codex (code-davinci-002) to surpass the average human-rater performance on 17 of the 23 tasks.”

3. Computer Science › Topic Modeling

   Transcending Scaling Laws with 0.1% Extra Compute

   Tay, Jason, Chung et al. · arXiv (Cornell University) · 2022

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   1. Unchecked“Impressively, at 540B scale, we show an approximately 2x computational savings rate where U-PaLM achieves the same performance as the final PaLM 540B model at around half its computational budget (i.e., saving $\sim$4.4 million TPUv4 hours).”
   2. Unchecked“Overall, we show that U-PaLM outperforms PaLM on many few-shot setups, i.e., English NLP tasks (e.g., commonsense reasoning, question answering), reasoning tasks with chain-of-thought (e.g., GSM8K), multilingual tasks (MGSM, TydiQA), MMLU and challenging BIG…